Name
Abstract | ||
|---|---|---|
Visual saliency has been an increasingly active research area in the last ten years with dozens of saliency models recently published. Nowadays, one of the big challenges in the field is to find a way to fairly evaluate all of these models. In this paper, on human eye fixations, we compare the ranking of 12 state-of-the art saliency models using 12 similarity metrics. The comparison is done on Jian Li's database containing several hundreds of natural images. Based on Kendall concordance coefficient, it is shown that some of the metrics are strongly correlated leading to a redundancy in the performance metrics reported in the available benchmarks. On the other hand, other metrics provide a more diverse picture of models' overall performance. As a recommendation, three similarity metrics should be used to obtain a complete point of view of saliency model performance. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICCV.2013.147 | ICCV |
Keywords | Field | DocType |
similarity metrics,visual saliency,jian li,state-of-the art saliency model,saliency model performance,active research area,kendall concordance coefficient,saliency model,human fixations,overall performance,comparison metrics,performance metrics,computer vision | Computer vision,Fixation (psychology),Ranking,Pattern recognition,Salience (neuroscience),Computer science,Redundancy (engineering),Artificial intelligence,Jian,Machine learning,Visual saliency | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1550-5499 |
Citations | PageRank | References |
52 | 1.35 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Riche | 1 | 184 | 9.75 |
| Matthieu Duvinage | 2 | 125 | 5.35 |
| Matei Mancas | 3 | 315 | 27.50 |
| Bernard Gosselin | 4 | 198 | 12.88 |
| T. Dutoit | 5 | 313 | 30.47 |